In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset. Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment. In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack. 
 Question: What is the training and test data used?
Tweets related to a Bank of America DDos attack were used as training data. The test datasets contain tweets related to attacks to Bank of America, PNC and Wells Fargo.

In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice – once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles). 
 Question: what lexical features did they experiment with?
TF.IDF-based features

While $\beta $-VAE offers regularizing the ELBO via an additional coefficient $\beta \in {\rm I\!R}^+$, a simple extension BIBREF16 of its objective function incorporates an additional hyperparameter $C$ to explicitly control the magnitude of the KL term,

where $C\!\! \in \!\! {\rm I\!R}^+$ and $| . |$ denotes the absolute value. 
 Question: How does explicit constraint on the KL divergence term that authors propose looks like?

Answer with content missing: (Formula 2) Formula 2 is an answer: 
\big \langle\! \log p_\theta({x}|{z}) \big \rangle_{q_\phi({z}|{x})}  -  \beta |D_{KL}\big(q_\phi({z}|{x}) || p({z})\big)-C|

